Biomorphic rhythmic movement controller

ABSTRACT

An artificial Central Pattern Generator (CPG) based on the naturally-occuring central pattern generator locomotor controller for walking, running, swimming, and flying animals may be constructed to be self-adaptive, by providing for the artificial CPG, which may be a chip, to tune its behavior based on sensory feedback. It is believed that this is the first instance of an adaptive CPG chip. Such a sensory feedback-using system with an artificial CPG may be used in mechanical applications such as a running robotic leg, in walking, flying and swimming machines, and in miniature and larger robots, and also in biological systems, such as a surrogate neural system for patients with spinal damage.

RELATED APPLICATION

This is a PCT application claiming priority based on U.S. application60/201,748 filed May 4, 2000, which is incorporated by reference herein.

Statement Regarding Government Funding

This invention was made under NSF grant number 9896362. The Governmentmay have certain rights in this invention.

FIELD OF THE INVENTION

The invention generally relates to robotics and movement control, andmore particularly, to rhythmic movement control systems that aresensitive and adaptive to the environment in which they are used.

BACKGROUND OF THE INVENTION

Basic rhythmic movements in animals are generated by a network ofneurons in the spinal cord called the Central Pattern Generator (“CPG”).Walking, running, swimming, and flying animals have a biologicallocomotor controller system based on a CPG, which is an autonomousneural circuit generating sustained oscillations needed for locomotion.Naturally-occurring CPGs have been studied and are beginning to beunderstood. Scientists have studied these naturally-occurring biologicalCPG systems and, in the early 1900s, articulated the basic notion ofsuch an oscillation-generating autonomous neural circuit for locomotion.T. G. Brown, “On the nature of the fundamental activity of the nervouscentres; together with an analysis of the conditioning of the rhythmicactivity in progression, and a theory of the evolution of function inthe nervous system,” J. Physiol., vol. 48, pp. 18-46 (1914).

An autonomous system of neurons can generate a rhythmic pattern ofneuronal discharge that can drive muscles in a fashion similar to thatseen during normal locomotion. Locomotor CPGs are autonomous in thatthey can operate without input from higher centers or from sensors.Under normal conditions, however, these CPGs make extensive use ofsensory feedback from the muscles and skin, as well as descending input.A. H. Cohen, S. Rossignol and S. Grillner, Neural Control of RhythmicMovements in Vertebrates (Wiley & Sons, 1988). Furthermore, the CPGtransmits information upward to modulate higher centers as well as tothe periphery to modulate incoming sensory information.

The CPG is most often thought of as a collection of distributedelements. For example, in the lamprey (a relatively simple fish-likeanimal), small, isolated portions of the spinal cord can generatesustained oscillations. When the spinal cord is intact, these smallelements coordinate their patterns of activity with their neighbors andover long distances.

It is well known that sensory input can modulate the activity ofnaturally-occurring, biological CPGs. Modulation of the CPG by sensoryinput can be seen quite clearly in the adjusting of the phase of theCPG. For example, as a walking cat pushes its leg back, sensors in theleg muscles detect stretching. These sensors (called stretch receptors)signal this stretch to the nervous system. Their firing initiates thenext phase of the CPG causing the leg to transition from stance to swingphase.

After some study of naturally-occurring biological CPG systems,scientists modeled CPGs as systems of coupled non-linear oscillators. Inthe early 1980s Cohen and colleagues introduced a model of the lampreyCPG using a system of phase-coupled oscillators. A. H. Cohen, P. J.Holmes and R. H. Rand, “The nature of the coupling between segmentaloscillators of the lamprey spinal generator for locomotion: Amathematical model,” J. Math. Biol., vol. 13, pp. 345-369(1982). Later,a model called Adaptive Ring Rules (ARR) based on ideas in Cohen etal.'s and related work was extended for use in robot control. M. A.Lewis, Self-organization of Locomotory Controllers in Robots andAnimals, Ph.D. dissertation, Dept. of Electrical Engineering, Univ. ofSouthern Calif., Los Angeles (1996). An ARR is a model of a non-linearoscillator at a behavioral level. Complex enough to drive a robot, anARR model also allows relatively easy implementation of learning rules.

Certain conventional non-biological (i.e., modeled) CPG-type chips andcircuits have been developed. For example, Still reported on a VLSIimplementation similar to a CPG circuit used to drive a small robot. S.Still, Presentation at Neurobots Workshop, NIPS*87, Breckenridge, Colo.,U.S.A. (1998); S. Still and M. W. Tilden, “Controller for a fourleggedwalking machine, in Neuromorphic Systems: Engineering Silicon fromNeurobiology, Eds: L. S. Smith and A. Hamilton (World Scientific,Singapore), pp. 138-148 (1998). Still et al.'s circuit captured somebasic ideas of a CPG, and Still's group demonstrated rudimentary controlof a walking machine. However, Still's system has no motoneuron outputstage, and cannot respond to or adapt based on sensory input.

Ryckebusch and colleagues created a VLSI CPG chip based on observationsin the thoracic circuits controlling locomotion in locusts. S.Ryckebusch, M. Wehr, and G. Laurent, “Distinct Rhythmic LocomotorPatterns Can Be Generated by a Simple Adaptive Neural Circuit: Biology,Simulation and VLSI Implementation,” J. of Comp. Neuro., vol. 1, pp.339-358 (1994). The resulting VLSI chip was used as a fast simulationtool to explore understanding of the biological system. Their system isnot a robotic system and cannot respond to or use feedback from sensors.

DeWeerth and colleagues have captured certain neural dynamics on adetailed level. G. Patel, J. Holleman, and S. DeWeerth, “Analog VLSIModel of Intersegmental Coordination with Nearest-Neighbor Coupling,” inAdv. Neural Information Processing, vol. 10, pp. 719-725 (1998). Thissystem of DeWeerth's cannot easily be applied to control a robot,primarily because parameter sensitivity makes such circuits difficult totune. To address this difficulty, DeWeerth et al. more recently haveimplemented neurons that self-adapt their firing-rate. M. Simoni, and S.DeWeerth, “Adaptation in a VLSI Model of a Neuron,” in: Trans. Circuitsand Systems II, vol. 46, no. 7, pp. 967-970 (1999). The adapted DeWeerthsystem, however, is not adaptive and does not use external inputs fromsensors.

U.S. Pat. No. 5,124,918, entitled “Neural-based autonomous roboticsystem” to Beer et al., teaches a system for controlling a walking robotusing a rhythmic signal used to coordinate locomotion with multiplelegs. Beer et al.'s neural-based approach, using software, is relativelybasic and does not teach, for example, VLSI implementation,self-adaptation to an environment, low-power compact implementationusing one chip, or silicon learning.

In recent years, robotics has been developing in many aspects, of whichsome have been mentioned above. Also, other challenges have beenidentified and are being studied in robotics, such as theminiaturization of walking, running, and flying robots, increasing thereal-time adaptability of robots to the environment, and the creation ofmass-market consumer devices. With these new robotics technologies comedemands for smaller, lower-cost, more power-efficient, more adaptivecontrollers, and, correspondingly, computational support.

Robotics has largely relied for computational support onmicroprocessor-based technology. Such systems have limitations, such asbeing unable to provide self-adaptive features.

Not necessarily connected with robotics technologies, a field ofneuromorphic engineering developed. Neuromorphic engineering usesprinciples of biological information processing to address real-worldproblems, constructing neuromorphic systems from silicon, the physics ofwhich in many ways resembles the biophysics of the nervous system.Neuromorphic engineering to date mostly has concentrated on sensoryprocessing, for example, the construction of silicon retinas or siliconcochleas.

Thus, interesting and exciting advances have been made in the thus-farrelatively separate technologies of robotics and neuromorphicengineering. However, work remains to be done to practically bringtogether these technologies. Conventional robotics and related systemsare non-adaptive to their environments, and theoretically the potentialexists for huge advances in the direction of increased adaptiveness tothe environment. Advanced robotics systems, control of mechanical limbs,control of biological limbs, rhythmic movement in biological systems,have been desired, such as increased responsiveness and relatedness tothe environment.

SUMMARY OF THE INVENTION

After investigating and considering how to make a modeled,non-biological CPG self-adapt, the present inventors arrived at thisinvention, in which a non-biological CPG tunes itself with sensoryfeedback The present invention provides miniature and non-miniaturerobotics systems, modeled non-biological systems that may be used inbiological applications, chips, movement machines, and other systemsthat receive sensory input from, and are adaptive to, an environment inwhich they are operating. For example, the present invention applies ARRtheory in designing such a man-made CPG chip. (Man-made CPG chips,systems and the like are referred to herein as “non-biological” and/or“CPG-based”.) It is a basic object of the present invention to constructnon-biological systems mimicking features of CPGs that occur in nature.(“Biological CPG” is used herein as a shortened way to refer to a CPGthat occurs in nature.)

It is a further object of the present invention to elucidate practicalapplications in which non-biological CPG systems may be used. Examplesof applications for non-biological CPG systems include use in biologicalsystems (such as the human body) as well as non-biological systems (suchas robots, movement machines, breathing controllers, and the like). Itis a further object of the present invention to provide for controllingone or more mechanical limbs via a CPG-based system, and to provide forcontrolling rhythmic movement in a biological system via a CPG-basedsystem.

A further object of the invention is to elucidate ways in which amodeled CPG system may be made adaptive and responsive to theenvironment in which it is used. As an important example, the inventionprovides advanced robotics systems and autonomous movement devices(including breathing controllers, running devices, swimming devices,flying devices, and other devices) with sophisticated responsiveness andadaptability to the environments in which they are used.

In order to accomplish these and other objects of the invention, thepresent invention in a preferred embodiment provides a CPG-based system,such as a CPG-based system for controlling at least one mechanical limb,comprising at least one mechanical limb and a non-biological CPG thatgenerates commands for controlling the at least one mechanical limbwherein commands are a function of sensory feedback. The inventionprovides a CPG-based system for controlling a biological system forrhythmic movement, comprising: (1) an interface with a biological systemthat can provide sensory feedback from said biological system, and (2) anon-biological CPG that generates commands for controlling thebiological system wherein commands are a function of sensory feedback.

In a particularly preferred embodiment, the invention provides for theCPG-based system to include a system for phase adjustment of the CPGbased on a sensory trigger in or derived from sensory feedback. Inanother particularly preferred embodiment, the CPG-based system mayinclude a system for phase adjustment of the central pattern generatorbased on at least one sensory trigger in or derived from sensoryfeedback; and a system for controlling firing frequency of motoneuronsas a function of the sensory feedback or the sensory trigger.

In a further embodiment, the invention provides for the CPG-based systemto include at least one memory device. In a preferred embodiment of theinvention, the memory device controls adaptation of output from the CPG.In a preferred embodiment of the invention, the output includesintegrate-and-fire neurons.

The invention also provides for another preferred embodiment in whichthe CPG-based system is at least one chip, and in another preferredembodiment, multiple chips. In a particularly preferred embodiment, theinventive chip contains electronic analogues of biological neurons,synapses and time-constraints. In another particularly preferredembodiment, the inventive chip includes dynamic memories and phasemodulators. Another preferred embodiment provided by the invention is asystem including at least one chip in which components are integratedwith hardwired or programmable circuits.

The invention in another preferred embodiment provides for the CPG-basedsystem to be a non-linear oscillator including electronic analogues ofbiological neurons, synapses and time-constraints, dynamic memories andphase modulators. The invention in a preferred embodiment provides aCPG-based system wherein the CPG is a distributed system of at least twonon-linear oscillators. In a further inventive embodiment, the inventionprovides for the distributed system to include at least one neuronphysically coupled to a neuron or a sensory input. In another inventiveembodiment, the distributed system includes at least two neuronsphysically coupled to each other, to another neuron, or to a sensoryinput. The invention provides in another embodiment for phasic couplingthat is in-phase, 180 degrees out of phase, or any number of degrees outof phase. In a particularly preferred embodiment, the invention providesphasic coupling based on rhythmic movement application. In an especiallypreferred embodiment, the invention provides for including a phasecontrol circuit. Where physically coupled nuerons are used, theinvention provides in another embodiment for including at least oneintegrate-and-fire spiking motoneuron driven by the physically coupledneurons.

The invention also provides in another embodiment for including at leastone muscle in the CPG-based system.

A particularly preferred embodiment of the present invention provides arobot.

The invention in a further embodiment provides for the CPG-based systemto include a CPG chip and at least one biological neuron. The inventionalso provides an embodiment in which a CPG-based system includes atleast one sensor for collecting sensory feedback. In a particularlypreferred embodiment, the CPG-based system includes a system for phaseadjustment of the central pattern generator based on at least onesensory trigger in the received sensory feedback. The invention providesan embodiment in which sensory feedback is received from a mechanicallimb or from a biological limb. The invention also provides anembodiment wherein the sensory feedback is received from a sensingmodality.

The invention also provides methods for controlling a mechanical orbiological system for rhythmic movement, such as methods comprising: (A)measuring sensory feedback to obtain measured sensory feedback; (B)processing the measured sensory feedback to obtain data for a pluralityof designated parameters; and (C) via a CPG-based system, applying a setof rules to the obtained data to generate at least one signal forcommanding the limb or biological system for rhythmic movement, whereinthe CPG-based system comprises a circuit that mimics a biological CPG.In a preferred embodiment, such an inventive method includes, via theCPG-based system, applying the generated signal to command the limb orbiological system for rhythmic movement. The invention also provides fora method wherein the CPG system comprises a circuit comprising at leasttwo coupled non-linear oscillators.

The invention also provides for further embodiments that are roboticssystems, such as a robotics system comprising: (a) a CPG-based systemthat mimics a biological central pattern generator; and (b) at least onesensory device. The invention also provides that in a particularlypreferred embodiment of the robotics system, the CPG-based systemreceives sensory input from the at least one sensory device.

The invention also provides autonomous movement devices, such as anautonomous movement device for providing rhythmic control, wherein theautonomous device comprise a non-biological CPG that generates rhythmiccontrol commands wherein commands are a function of sensory feedback.The invention in a further embodiment provides for the autonomousmovement device to include at least one mechanical limb. In anotherembodiment, the invention provides that the limb is a leg, arm, wing orappendage for swimming. In some embodiments of the invention, at leasttwo limbs are included. The invention in other embodiments provides abreathing controller, a pacemaker, and a running device.

The invention also provides a non-biological CPG comprising a memorydevice; and a system for manipulating neural phasic relationships.

Further, the invention provides a method for modifying a continuouswaveform provided by a non-biological CPG, comprising the steps of: (A)provision of a continuous waveform by a non-biological CPG; (B)provision of sensory feedback to the non-biological CPG; (C)rule-application by the non-biological CPG to the sensory feedback; (D)based on the rule-application, determination by the non-biological CPGto modify or maintain the continuous wave form. In a particularlypreferred embodiment of such a method for modifying a continuouswaveform, the invention provides for the non-biological CPG to modifythe wave form. In another embodiment that is particularly preferred, theinvention provides for the the rule-application to be the application ofadaptive ring rules.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of the preferredembodiments of the invention with reference to the drawings, in which:

FIG. 1 depicts the layout of an example of a CPG chip according to theinvention.

FIG. 2 is a schematic of an example of an integrate-and-fire motoneuronand synapse according to the invention.

FIG. 3 is an example of a circuit diagram which is according to theinvention and adaptively controls dynamics of a limb, by a neuralnon-biological CPG with learning capabilities.

FIG. 4 is a hip, knee and foot-contact phase diagram for arepresentative embodiment according to the invention.

FIG. 5 is a series of graphs, for a hip, knee, and foot, showing theeffect of lesioning sensory feedback when the invention is used.

FIGS. 6(a) and 6(b) are plots of hip position versus time for a robotaccording to the present invention.

FIG. 7(a) is a flow chart showing an exemplary relationship of a CPGchip according to the invention and sensory input. FIG. 7(b) is a flowchart showing a simple example of processing that occurs for informationcollected by a sensor according to the invention.

FIG. 8 is a cross-section view showing use of a CPG chip according tothe invention in a human patient with a damaged spinal cord.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

The invention provides a non-biological CPG-based system which isadaptive to the environment in which it is used. To be adaptive, the CPGsystem receives sensory feedback from the environment and acts based onthe received sensory feedback. In one preferred embodiment, theinvention provides a CPG-based system that controls one or moremechanical limbs. In another preferred embodiment, the invention uses aCPG-based system for controlling rhythmic movement in a biologicalsystem, such as one or more biological limbs or structures. Walking,swimming, flying, hopping and breathing machines, by way of non-limitingexample, maybe made using the invention. In the non-biological CPG-basedsystem, a non-biological CPG generates commands that are a function ofsensory feedback.

“Sensory feedback” as mentioned in the present invention refers to anysensory information that is, or is processible into information that is,recognizable by a non-biological CPG. Sensory feedback may be from amechanical source (such as a mechanical limb, camera or other artificialvision, artificial audition, artificial muscle sensors, etc.) or abiological system (such as neural signals from muscles, neural signalsfrom brain regions, etc.). In a preferred embodiment of the presentinvention, sensory feedback from multiple sources is provided to anon-biological CPG.

For obtaining the sensory feedback from a mechanical source, one or moresensors may be provided for collecting sensory feedback. The sensor mayreceive the sensory feedback from any sensing modality. A sensor for usein the invention is not particularly limited and maybe artificialvision, artificial audition, artificial muscle sensors, or sensors formeasuring any natural or environmental condition (such as weather, airquality or content (e.g., acidity), presence of chemicals, fire,temperature, pressure, lighting conditions, gunfire, microwaves, opticalinformation, etc.), contact sensors, etc.

When a sensor is used, the positioning of the sensor is not particularlylimited with regard to the non-biological CPG, and the sensor and theCPG maybe disposed in any manner in which they are in communication witheach other, directly or indirectly. The manner in which communicationbetween a sensor and a non-biological CPG may be established, directlyor indirectly, is not particularly limited, and the sensor andnon-biological CPG may be connected electrically such as by being wiredto each other. In an example of a CPG chip according to the inventionshown in FIG. 1, all the components are individually accessible suchthat they can be connected with off-chip wiring to realize any desiredcircuit. Neural CPG circuits can be integrated with completely hardwiredor programmable circuits. The sensor and the CPG chip may be connectednon-electrically, such as optically, by a wireless connection, byinfra-red, chemically, or connected electrically and chemically.

The non-biological CPG and the sensor communicate in a languageunderstood by both, e.g., spike-coded, digital interface, analoginterface, etc. The language maybe selected based on the non-biologicalCPG and the sensor being used. One skilled in the art is familiar withestablishing an interface according to the components to be put incommunication with each other, such as the non-biological CPG and thesensor.

In a preferred embodiment, a sensor may be used in relatively closeproximity to the non-biological CPG, for providing information about theimmediate environment of a CPG-based system according to the invention,or of a patient in which a CPG-based system is operating.

However, it will be appreciated that in some applications it may beadvantageous to operate with a substantial distance between a sensor anda non-biological CPG. For example, a sensor may be placed on or in anobject or person who may need rescuing, to be used, if needed, incooperation with a moving rescuer device comprising a non-biologicalCPG.

A non-biological CPG for use in the present invention is at least onecircuit that is pattern-generating and further is configured to generatecommands (such as self-adapting) as a function of sensory feedback. Thepattern-generating of the non-biological CPG in a most preferred examplemay be provision of continuous waveforms, with the waveforms being afunction of one or more waveform parameters. A waveform parameter may beany parameter that accomplishes an adjustment associated with apredetermined feature of movement, and may depend on the movement. Forexample, in the case of two-legged movement, waveform parameters mayinclude “center of stride”, “left/right” adjustment, “length of stride”,“frequency of stride”, “height”, etc. Those skilled in the art arefamiliar with generally selecting a wave-form for a non-biological CPGto match and thus provide a particular desired motion, such as selectinga waveform to provide a running motion with a certain center, length andfrequency of stride. In the present invention, it is preferred that thenon-biological CPG be configured to provide a variety of continuouswaveforms and to easily self-reset from one waveform being generated toanother waveform.

Preferably, a non-biological CPG for use in the invention is oneconfigured with safeguarding to only permit generation of waveformswithin the capabilities and performance specifications of the system inwhich it is used, such as not permitting hyper-extension, not generatinga wave-form that would overstretch or otherwise harm a patient in whichit is implanted, etc. On the other hand, preferably a non-biological CPGfor use in the invention within such a safeguarded range has a richarray of waveforms, to provide fine movement details.

To be suitable for use in the present invention, the non-biological CPGis configured to generate commands as a function of sensory feedback.For example, as seen with reference to FIGS. 7(a) and 8, a CPG chip 1which is a preferred embodiment of the invention may be used in abiological system such as a human patient. The CPG chip 1 may receivesensory feedback from neural signals from muscles 6, artificial vision7, artificial audition 8, artificial muscle sensors 9, and neuralsignals from brain regions 10. The CPG chip 1 may issue commands 100 tomuscles and limbs 11. A dotted line shows direct and indirect influence110 of the sensory feedback.

When a CPG chip is used in a biological system, such as shown in FIGS.7A and 8 by way of example, an interface between the CPG chip and thebiological system is provided. The interface is one that can providesensory feedback from the biological system to the CPG chip in the formof electrical signals. Measurement of electrical activity in neurons isknown in neurophysiology and such measurement techniques may be used inthe invention. The activity of neurons can be sensed using electronicprobes that measure the electrical discharge of the cells. These signalsare usually small, and may be amplified so that they can be provided tothe CPG chip as voltage spikes. The interface with the biological systemmay be any interface capable of providing voltage spikes to the CPG,such as chronically implanted micro electrodes that may be used tomeasure the electrical activity of neurons in the muscles, spine orbrain. The microprobes may be connected, with thin wires, to apre-amplifier chip that magnifies the signals from millivolts to volts.The magnified signals may be presented to the CPG chip as pulses to thesynapses.

The non-biological CPG of the present invention comprises anybiologically plausible circuit or circuits for controlling motorsystems. The definition of a non-biological CPG in terms of biologicalplausibility for controlling motor systems is not intended to limit anon-biological CPG according to the invention to motor systemsapplications.

An example of a non-biological CPG for use in the invention is a memorydevice combined with a system for manipulating neural phasicrelationships, such as a non-biological CPG that maintains a continuouswave-form and self-adapts the waveform based on sensory feedback.

As has been said, the non-biological CPG for use in the presentinvention is one that generates commands as a function of sensoryfeedback. In a preferred embodiment of the present invention, togenerate such commands, the sensory feedback which consists ofelectrical signals is evaluated to determine what, if any, effect thesensory feedback is to have on the wave form being provided. Thenon-biological CPG may be programmed to recognize one or more sensorytriggers in the sensory feedback, and for each particular sensorytrigger found, to respond according to a predetermined rule. For suchevaluation of the sensory feedback, programming for cyclic readjustmentof signals, such as ARR, may be applied. ARR programming preferably isused for such evaluation of the sensory feedback. Sufficient memoryand/or data storage are provided to support evaluation of the sensoryfeedback.

By way of a non-limiting example, a non-biological CPG according to thepresent invention may be programmed to recognize in sensory feedbackfrom a visual sensor a certain pattern as a hole, to treat the hole as acertain sensory trigger, and therefore to adjust the waveform inprogress according to a certain pre-determined rule or set ofpredetermined rules.

Methods of evaluating and characterizing sensory feedback known to thoseskilled in the art may be used in the present invention, such as knownmethods of characterizing data from an artificial vision system such asa camera, known methods of characterizing data from an artificialaudition system, etc.

A non-limiting example of a CPG-based system according to the inventionis as follows, discussed with reference to FIGS. 7(a), 7(b) and 8. Whenthe system is started, the CPG chip begins running an initial continuouswaveform. A sensor (such as artificial vision 7 in FIGS. 7(a) and 8)collects sensory information and, directly or indirectly (such as afterprocessing to be readable by the CPG chip) sends sensory feedback to theCPG chip. The sensory feedback may be sent from the sensor to the CPGchip directly such as by a circuit. Sensory feedback information istransmitted for receipt by the CPG chip in the form of electricalsignals.

As shown on FIG. 7(b), in a preferred embodiment of the invention, a CPGchip receives sensory feedback from one or more sensors 60. The CPG chipreceives 70 the sensory feedback in the form of electrical signals, andthen applies ARR 80 relating to predetermined sensory triggers to thesensory feedback. Based on sensory triggers in the sensory feedback, theCPG chip determines 90 whether to modify or maintain the continuouswaveform that is in progress. If the CPG chip determines to maintain 91the waveform in progress, no change to any waveform parameter iscommanded. If the CPG chip determines to modify 100 the waveform inprogress, the CPG chip alters one or more waveform parameters, such as,in this example, “forward displacement”, “center of stride”,“left/right”, “length of stride”, “frequency of stride”, “height”, etc.

Most preferably, the CPG applies ARR to the sensory feedback in as shorta time as possible. For example, if a sensor in a walking machineprovides sensory feedback indicating a hole, desirably such sensoryfeedback is processed and acted on before the walking machine travelsinto the hole.

With reference to FIG. 7(b), information resulting from modification bythe CPG 100 of 1 or more waveform parameters is sent to the sensors 60.In FIG. 7(b), by way of example, sensory feedback is shown with regardto sensors 60, but it will be appreciated that the sensors 60 are onlyan example of a source of sensory feedback and may instead be abiological system, or a combination of sensors and a biological system.

As may be appreciated with reference to FIGS. 7(a) and 7(b), based onsensory feedback, a CPG-based system according to the inventionself-adapts. The self-adapting is not particularly limited, and maycomprise any command (such as a waveform phase adjustment or otheraction that the CPG undertakes) that is a function of sensory feedback.In a preferred example, the self-adapting comprises a system for phaseadjustment of the CPG based on a sensory trigger in or derived fromsensory feedback, or a system for controlling firing frequency ofmotoneurons as a function of the sensory feedback or the sensorytrigger.

In a preferred embodiment of the invention, at least one memory deviceis used. Preferably, the memory device is one that controls adaptationof output such as output parameters from the CPG. The memory device maybe a short-term memory device. Preferably, a high level of abstractionis used to more easily implement on-chip learning. Systems based onnumerous inter-related parameters are avoided because in such systems itis not apparent how learning at the level of behavior can be coupled tolow level parameter changes. The memory device may be a dynamic analogmemory, or a digital memory device.

A CPG-based system according to the invention is not particularlylimited in its form, and may be in the form of one or more chips, arobot, a movement machine (such as a walking, running, swimming, flyingor breathing machine), a biological system etc.

An example of a preferred embodiment of the invention is a CPG chip 1 asshown in FIGS. 1, 7(a) and 8. As seen with reference to FIG. 1, the CPGchip 1 includes phase controller 2 and pulse integrator 3. Burstduration neurons 4 and integrate-and-fire spiking neurons 5 are providedon CPG chip 1 A schematic for an integrate-and-fire spiking neuron 5 isshown in FIG. 2.

In FIG. 2, on the right-hand side, hysteretic comparator 5 a, spiketrain 5 b and spike reset 5 c of neuron 5 are shown. The left-hand sideof FIG. 2 shows the schematic of the synapse 12 for neuron 5. Excitoryspike 12 a and excitory weight 12 b, and inhibitory weight 12 c andinhibitory spike 12 d, are shown for synapse 12.

A chip used in a CPG-based system according to the invention may includedynamic memories and phase modulators. A CPG chip according to thepresent invention may be integrated with hardwired or programmable ocircuits, or may be used in any other form. In FIG. 1, the CPG chip isshown with each component wired to pins to facilitate the prototyping ofoscillator circuits.

In a preferred embodiment of the invention, the CPG-based system is anon-linear oscillator based on the CPG of a biological organism. In asystem comprising a non-linear oscillator, the system is non-linear andpreferably includes a chip using non-linear elements, to provide acoupled system ofnon-linear elements, without linearizing the system.When a non-linear oscillator is used, because linearization is not used,instead principles from biological systems are used, which can beimplemented easily with low-power integrated circuits, to provide acompact system.

The system may include electronic analogues of biological neurons,synapses and time-constraints. FIG. 2 depicts some examples of suchelectronic analogues.

The CPG-based system of the invention may be a distributed system of atleast two non-linear oscillators, of which FIG. 3 is an example. Thecircuit of FIG. 3 adaptively controls dynamics of a limb, by a neuralnon-biological CPG with learning capabilities. Other distributed systemsof at least two non-linear oscillators may be used in the invention.Such a distributed system may include at least one neuron physicallycoupled to a neuron or a sensory input. Preferably, the distributedsystem includes two or more neurons physically coupled to each other, toanother neuron, or to a sensory input.

The phasic coupling used in the invention maybe in-phase, 180 degreesout of phase, or any amount out-of-phase. The phasic coupling may beselected based on desired end-use application such as a particularrhythmic movement. When an integrate-and-fire spiking motoneuron isused, a preferred integrate-and-fire spiking motoneuron is one driven byphysically coupled neurons. The phasic coupling feature may be providedas a phase control circuit, such as phase controller 2 in FIG. 1. Phasecontrol circuits and phasic coupling are known to those skilled in theart.

The inventive CPG-based system may include one or more mechanical orbiological limbs, examples of which are seen in FIGS. 7(a) and 8. Theinventive CPG-based system is not limited to limbs, and in the case ofbiological systems, may include any biological system for rhythmicmovement, in which case FIGS. 7(a) and 8 still are relevant, butreplacing reference to a limb with that of an organ or other componentof the body.

The CPG-based system according to the invention is especially suited foruse in a biological system, such as the human body or an animal. In suchan application, the CPG-based system may include one or more muscles,biological neurons, etc.

Above the invention has been discussed with regard to FIG. 1, showing asingle CPG chip. It will be appreciated that FIG. 1 is an example andthat the invention may be practiced using two or more chips. Whenmultiple chips are used for constructing a CPG-based system according tothe invention, those chips may be electrically connected as is known tothose skilled in the art.

The invention also maybe practiced by constructing a robot, such as arobot comprising one or more CPG chips according to FIG. 1. For making arobot according to the invention, a CPG-based system that mimics abiological CPG (such as a chip like that of FIG. 1) may be electricallyconnected with one or more sensors, and further may be connected withone or more memory devices. The memory device preferably is programmedwith a set of adaptive ring rules relating to predetermined triggersthat maybe found in the sensory feedback from the particular sensorsthat are being used.

In another preferred embodiment, the invention provides a method forcontrolling a mechanical limb or biological system for rhythmicmovement, by (A) measuring sensory feedback; (B) processing the measuredsensory feedback to obtain data for at least one designated parameter;and (C) via a CPG-based system, applying a set of rules to the obtaineddata to generate at least one signal for commanding the limb orbiological system for rhythmic movement. The processing may be to obtaindata for a plurality of designated parameters, or, in a most simpleexample, to obtain data for one parameter. The CPG-based system used maybe any system comprising a circuit that mimics a biological CPG. Such aninventive method preferably includes a further step of, via theCPG-based system, applying the generated signal to command the limb orbiological system for rhythmic movement. For practicing such a method,preferably the CPG-based system comprises a circuit comprising at leasttwo coupled non-linear oscillators.

In another preferred embodiment, the invention may be used to constructan autonomous movement device for providing rhythmic control. Such adevice is constructed starting with a non-biological CPG that generatesrhythmic control commands that are a function of sensory feedback, towhich is added one or more movement components, such as one, two or moremechanical limbs, which maybe a leg, arm, wing or appendage forswimming, or other limb. Examples of such a movement device may be arunning device, a flying device, a hopping device, a jumping device, awalker, a breathing controller or a pacemaker.

It will be appreciated that the uses of the present invention are notparticularly limited, and examples maybe biological and medicalapplications, movement and transportation applications, rescueapplications, space exploration, toys, etc. However, such examples ofuses of the present invention are not intended to be limiting.

As an example of a biological or medical application, the presentinvention in a particularly preferred embodiment may be used in treatingpatients with spinal total or partial spinal damage, such as byproviding a spinal neural stimulator for paraplegics with a transectedor crushed spinal cord. A chip according to the invention may be used tostimulate nerve cells that are responsible for walking. Such a chipoutputs signals that are compatible with biological neural circuits.

Another preferred use of the present invention is to provide a chip thatmay control a leg vehicle (such as that of Iguana Robotics, Inc.,PowerBoots technology) that assists the normal process of running, suchthat individuals can run faster, longer, jump higher while carrying moreweight. Such a leg vehicle has applications in both civilian andmilitary arenas. The adaptive properties of a control system accordingto the present invention allows a leg vehicle such as PowerBoot to“learn” the individual dynamics of the user and continuously fine-tuneitself to optimize running efficiency, speed and power source lifetime.In addition, using the present invention in a leg vehicle may decreasethe stress placed on the user by allowing the user to also modify thebehavior of the controller through direct inputs.

It will be appreciated that uses of the invention in biological ormedical applications are not necessarily limited to those in which abiological CPG was or is operating (such as for locomotion control) butalso may be extended to uses in which a biological CPG may not have beeninvolved, such as timed-release and other drug delivery (such as in thecontext of stimulating nerves). For example, a non-biological CPG systemaccording to the invention maybe implanted in the brain or spinal cordfor accomplishing drug delivery. Also, the invention may be used inneuro-stimulation applications, for controlling epilepsy, and forchemical-sensing in a patient.

Another preferred embodiment of the invention uses a non-biological CPGchip as a control system for a robot, such as walking robot. In aparticularly preferred embodiment, the robot can navigate rough terrain.For navigating such terrain, a multi-pedal walking machine may be used,with a controlling comprising a non-biological CPG chip that coordinatesthe limbs and adapt their behaviors based on the environment. Withvisual inputs, the robots can use adaptive CPG chips to run and hurdleobstacles at high speeds.

The present invention in another preferred embodiment is used in medicaland biological applications, such as an implantable, neurologicallycompatible neural surrogate for a paralyzed individual. Such a neuralsurrogate according to the invention has low power consumption and abiologically based nature and may be used to regulate breathing, heartbeat and other rhythmic movements. An implantable neural surrogateaccording to the invention also may be provided to adapt itself tooptimize its own efficiency and that of the biological systems itcontrols.

The invention also may be used in miniature systems to modulaterepetitive cyclical movements based on sensory feedback, such asminiature walking, running, flapping, flying and swimming machines.Ultra-small, adaptable control systems are preferred for such robots,for various reasons (such as providing an obtrusive device or to fitinto a limited space). The present invention can provide such controlsystems. Miniature robots and robotics systems may be used forreconnaissance, and search missions (such as in a fallen building afteran earthquake), and will benefit from a compact control system accordingto the present invention. Also, miniature embodiments of the presentinvention may be used in surgical applications, such as a catheterprovided with a miniature CPG chip controlling how the catheter contactsand wiggles as it “swims” in blood.

In other embodiments, the invention may be used in toy applications,such as a toy animal comprising a controller according to the invention.Because the invention can provide a controller that is relatively smalland low power, the controller can be mounted directly on toy robot limbsto be controlled. In addition, its low power nature means that it willnot drain the batteries quickly. The adaptive aspect means that the toyrobot animal (such as a “Tabby” cat) can change its gait based on theobstacles and type of environment in which it is walking, without usingany CPU, using controls that are biologically inspired, and using adistributed network of autonomous controllers that are coupled throughthe dynamics of the toy robot and the properties of the environment.

EXAMPLE NO. 1

A robot comprising a biomorphic leg was constructed using a neuromorphicchip on which CPGs were modeled as distributed systems of non-linearoscillators. To provide basic coordination in a leg, two neurons werephysically coupled together to achieve oscillations. They were coupledtogether to be alternatively active, with the alternating activity asthe basic coordination that drove the hip of the robot. A phase controlcircuit governed the phase difference between the neurons. Theoscillator neurons drove two integrate-and-fire spiking motoneurons,which drove an actuator. (The spiking neuron could also drive biologicalmuscle or it could also be used to drive a pneumatic cylinder, aMcKibben actuator or biomuscle directly).

The robot used servomotors to provide electrical power. To be compatiblewith this technology, low-pass filters 14 were applied to the spikingneurons and the resulting smooth graded velocity signal was integrated.

The circuit was used in autonomous operation and with sensory feedbackfrom stretch receptors used to adjust the CPG. Properties of theconstructed biomorphic leg were demonstrated. The biomorphic limb andits control circuit produced stable rhythmic motion, and alsocompensated for intentional biases in the chip as well as mechanicalcomplexity of an active hip and passive knee.

As basic components of the robot, neurons and a CPG chip were used witha robotic leg.

Neurons

The neuron used was an integrate-and-fire model. A capacitor,representing the membrane capacitance of biological neurons, integratedimpinging charge. As seen with reference to FIG. 2, the capacitor wasset that when the “membrane-potential” exceeds the threshold of ahysteretic comparator 5 a, the neuron output is high. In this circuit,this logic high triggers a strong discharge current that adjusts themembrane potential to below the threshold of the comparator, thuscausing the neuron output to adjust. This circuit therefore emulated theslow phase and fast phase dynamics of real neurons, with the processthen starting anew. FIG. 2 shows a schematic of the neuron circuit usedin the robot of Example 1.

The neurons used in Example 1 were those that carry activationinformation in the frequency of spikes. The rate at which the membranepotential charges up controls the firing frequency of the neuron. Thisrate is given by the sum of the total charge flowing in and out of themembrane capacitance. The strength of the reset current sourcedetermines the width of each neural spike. The discharge current isusually set to a large value so that each spike is narrow and is notinfluenced by the charge injected onto the membrane capacitor.Typically, the neuron is set to fire at a nominal rate at rest, withadditional input increasing or decreasing the firing rate, and withshunting inhibition that can also silence the neuron.

The following equation (1) gives the dynamic equation for the neuron inExample 1. $\begin{matrix}\begin{matrix}{{{(a)\quad C_{i}^{blue}\frac{\mathbb{d}V_{l}^{mem}}{\mathbb{d}T}} = {I_{spoon} - {S_{l}I_{dis}} + {\sum\limits_{j}{{\overset{\_}{S}}_{j}I_{j}^{+}}} - {\sum\limits_{j}{S_{j}I_{j}^{-}}}}}\quad} \\{{(b)S_{i}} = \left\{ \begin{matrix}1 & {{{if}\quad V_{t}^{mem}} > V_{T}^{+}} \\0 & {{{if}\quad V_{t}^{mem}} < V_{T}^{-}}\end{matrix} \right.}\end{matrix} & (1)\end{matrix}$With reference to equation (1), there are three input voltages: (1) afeedback input from a hysteretic comparator (S_(i)), (2) Excitatoryinputs from other neurons (S_(i)) and (3) Inhibitory Inputs from otherneurons ( S _(i)) These inputs are weighted by current sources. Thesecurrent sources are denoted I_(dis), I_(i), and I _(i) respectively. Inaddition, a constant current injection sets a spontaneous spike rate ofthe neuron. As noted above, I_(dis) sets the spike duration. Finally,the term V_(T) ⁺ and V_(T) ⁻ set the thresholds for the hystereticcomparator respectively.

The spike trains impinging on a neuron activate switches that allowcharge quanta to flow into or off the membrane capacitor. The amount ofcharge transferred per spike is the synaptic weight and is controlled byan applied voltage that regulates the current sources. Modulation ofthis voltage allows the adaptation of the neural firing rate and is usedduring learning. The left-hand side of FIG. 2 shows the schematic of thesynapse 12, while equation (1) above shows how the neuron is affected bythe synaptic weight.

In addition to spiking neurons, neurons with graded response also wereused in making the robot of Example 1. The graded-response neurons wereessentially the same as the spiking neuron except for replacing thehysteretic comparator with a linear amplifier stage and not usingfeedback voltage.

Oscillators on the CPG Chip

The neural circuits for creating the CPG were constructed usingcross-coupled square-wave oscillators, with the output of theseoscillators driving the bursting motoneurons described above. Amaster-slave configuration of the neurons was used, to allowconstruction of an oscillator with a constant phase relationship. Bysetting the excitatory and inhibitory weights to equal values, asquare-wave with a duty-cycle of 50% was obtained. The phaserelationship between the two sides was subject to being varied. Thefrequency of oscillation was set by the magnitude of the weights. Thisasymmetrically cross-coupled oscillator served as the basic CPG unit,with the oscillator subject to being modified according to theapplication so that if a different application was desired later, theoscillator could be reset. By injecting or removing charge from themembrane capacitors of the oscillator neurons, the properties of the CPGcould be altered.

To be able to produce more complex waveforms, a phase controller wasincluded on the chip. This phase controller allows the phase differencebetween oscillators to be set arbitrarily. For the experiments describedherein, a strict 180 degrees phase relationship was needed, hence, aninverted version of an oscillator was used, as shown in FIG. 3.

Neural Circuit on the CPG Chip

The complete neural circuit as used in making the robot of Example 1 isshown in FIG. 3. The output of the basic oscillator unit 13 was used toinhibit the firing of the spiking motoneuron. The oscillator 13 was setso that when the oscillator output is high, the motoneuron is notallowed to fire, which produces two streams of 180 degrees out of phasespike trains. These trains could be low-pass filtered to get a voltagewhich could be interpreted as a motor velocity. Consequently, theoscillator controlled the length of the motor spike train, while thespike frequency indicated the motor velocity. The spike frequency wasregulated by a feedback loop. Spiking placed charges on the neuronmembrane capacitor seen in the lower part of FIG. 3. The integratedcharges were buffered and then used to down regulate spike frequency. Inthis way spike frequency was less sensitive to component variations.

Four neurons were provided as described above, in the form of a customVLSI CPG chip occupying less than 0.4 square mm.

Robotic Leg

In assembling the self-adaptive robotic leg of Example 1, a robotic legthat was a small (10-cm height) two-joint mechanism was combined withthe above-mentioned CPG chip via components to interface the chip to therobotic leg, and a data collection facility. In the robot of Example 1,only the “hip” was driven with the “knee” being completely passive andswinging freely, rotating on a low friction ball-bearing joint. A hardmechanical stop prevented the knee from hyper-extending.

In Example 1, the neurons of the CPG chip were interfaced to aservomotor using a rudimentary muscle model. The muscle dynamics weresimulated as a low pass filter to smooth the output of the spikingneurons. This was followed by an integrator, implemented in software, toconvert the velocity signal to a position command needed by theservomotor. A bias (intended to be typical of uncompensated parametersin a chip) was intentionally introduced into the chip to cause anasymmetry in the backward and forward swing of the leg.

The robotic leg of Example 1 was provided with three sensors. Two LVDTsensors monitored the position of the knee and hip joints. LVDT sensorswere used because they introduced minimal friction and had infiniteresolution. Additionally, the robot was provided with a miniatureload-cell sensor that monitored ground forces. The units of the loadcell are uncalibrated in all figures.

Experimentation

Using the inventive robot of Example 1, two additional sensory mediatedloops that adapt the oscillator and the motoneuron spiking were added,and testing relating to sensory adaptation and learning was performed.The inventive circuit used in the experimentation was one consuming lessthan one microwatt of power and occupying less than 0.4 squaremillimeters of chip area.

Adaptation-based “Stretch Receptor”

As shown in FIG. 3, the oscillator neurons of the robot of Example 1could be stopped or started with direct inhibitory and excitatorysensory inputs, respectively. When the inputs were received as stronginhibition, the membrane capacitor was shunted and dischargedcompletely. It remained in this state until the inhibition was released,then the normal dynamics of the oscillator continued from the inactivestate. On the other hand, if the sensory input was received as a strongexcitation, the oscillator was driven into an active state. When theexcitation was released, the oscillator continued from the active state.The charge-up or discharge of the membrane capacitor was influenced byany direct sensory input. For periodic sensory inputs, the oscillatoroutputs could be driven such that they phase locked to the inputs. Thusthe oscillator was entrained to the dynamics of the system undercontrol.

This property of the oscillator being entrained to the dynamics of thesystem under control was used to mimic the effect of the stretch reflexin animals. When the leg of an animal is moved to an extreme position, aspecial sensor called a stretch receptor sends a signal to the animal'sCPG causing a phase adjustment. This biological phase adjustmentresponse is mimicked in the circuit of Example 1. Referring to FIG. 3,the biomorphic leg of Example 1 may reach an extreme position whilestill being driven by the oscillator. In this case, virtual positionsensors 16, which mimic stretch receptors, send a signal to ResetA 15 aor ResetB 15 b to cause an adjustment of the oscillator circuit asappropriate to cause a hip joint velocity reversal.

Spike Freguency Adaptation

To provide learning, the chip included a short-term (on the order ofseconds) analog memory to store a learned weight. This architecturefavors a continuous learning rule. Spikes from the motoneurons were usedto increase or decrease a voltage on a capacitor; the voltage was usedto set the connection weight of another neuron. In the absence of thetraining inputs, the stored weights decay at approximately 0.1V/s. FIG.3 shows a schematic for adapting the spiking frequency of themotoneurons based on the swing amplitude of the limb.

In FIG. 3, the limb was driven back and forth with a velocity signalthat was obtained by low-pass filtering the activity of the motoneurons.Because the CPG oscillator fixed the duration of the spike train,changing the spiking frequency of the motoneuron altered the amplitudeof the velocity signals, which in turn varied the swing amplitude of thelimb. If the amplitude of swing did not reach the maximum positions, themotoneuron spike rate was increased. An increase in spike rate was keptbounded by negative feedback to the learning circuit. When the swingamplitude reached maximum, the positive input to the learning circuitwas reduced, thus allowing the spiking rate to settle to a constantvalue. The continuous negative feedback of the spike rate and the inputfrom the position detectors maintained the learned spiking rate. Theduration of the burst component of the spike train was furthercontrolled by feeding the position signals directly to the CPGoscillators to reverse the trajectory of motion at the end points. Thisallowed very asymmetric forward and backward velocity signals to beadaptively re-centered.

Set-Up

The small robotic leg of Example 1 was used for the experimental set-up.The output of the hip LVDT was sampled digitally. The signal wasinterval coded. Two intervals were selected as representing the extremesof movement of the hip (called “virtual position sensor” in FIG. 3).When these extremes were reached, the corresponding interval was active.This interval then sent a signal to the CPG chip causing an appropriateadjustment.

An oscillator frequency was selected by hand to be approximately 2-3 Hz.This frequency would excite the mechanical structure and cause the legto “run” a rotating drum. In practice the leg was not highly sensitiveto this excitation frequency but no effort was made to quantify thissensitivity.

Experiment 1: Running with a Passive Knee

With Example 1 in the above experimental setup, the CPG circuit was setto drive the actuator in the hip joint. The knee joint was passive androtated with very little friction. The assembly was suspended above arotating drum. The CPG circuit was started, and data was collected forthree sensors, including foot pressure, knee and hip. “Stretch receptor”sensory feedback from the hip was used as feedback to the CPG.

Running with the passive knee included a notable result that in thesystem of Example 1 according to the present invention, the knee jointadapted the correct dynamics to enable running. As the upper limb swungforward, the lower limb rotated so that the foot came off the ground.When the upper limb was suddenly accelerated backward, the momentum inthe lower limb forced the knee to lock in place. At just the correctmoment, the foot contacted the ground and the subsequent loading keptthe knee joint locked in place. As the foot traveled backward iteventually began to unload. Stored energy in the elastic foot caused itto “kick up” and smartly snap off the ground, an effect most noticeableat higher velocities.

FIG. 4 shows a phase plot of the knee, foot and hip position and footcontact for the robot of Example 1 when Experiment 1 was performed. InFIG. 4, most of the trajectory is in a tight bundle, while the outlyingtrajectories represent perturbations. The bulk of the trajectorydescribes a tight ‘spinning top’ shaped trajectory while the fewoutlying trajectories are caused by disturbances. After a disturbancethe trajectory quickly returns to its nominal orbit, which reflects thatthe system was stable.

Experiment 2: Sensory Feedback Lesioning

Experiment 1 was repeated except for lesioning (turning off) sensorfeedback periodically. Data was collected as in Experiment 1.

FIG. 5 shows the effect of lesioning sensory feedback on the position ofthe hip and knee joints as well as the tactile input to the foot. Afterlesioning the leg drifted backward significantly due to a bias builtinto the chip. When the sensory input was restored, the leg returned toa stable gait. When the feedback was lesioned (Time 11-19 seconds and31-42 seconds), the hip drove backward significantly. As it did the footbegan to lose contact with the surface and the knee stopped moving. Whenthe lesion was reversed at 19 and 42 seconds, the regularity of the gaitwas restored.

FIGS. 6(A) and (B) show the effect of perturbations on gait with intactand lesioned sensory feedback. In FIG. 6(A), five sequentialtrajectories (numbered) in intact and lesioned conditions arerepresented as ranging between black and light gray. A perturbation at 2in the intact case lead initially to worse trajectories (3 and 4), butquickly stabilized to the nominal orbit (5). In the lesioned case, chipbias caused a perturbation at 2 from which the gait could not recover;the hip was forced backward (3,4, and 5). In FIG. 6(B), the same tentrajectories shown in FIG. 6(A) are presented as hip positions throughtime, with various knee positions numbered. Intact sensory feedbackpermitted recovery while lesioning caused drift of both the hip andknee.

The experimentation provided the following information about gaitstability for the robot of Example 1. Perturbations to the leg causedmomentary disturbances. As seen in FIG. 4, several of the trajectoriesare clear “outlyers” to the typical orbit, and resulted fromenvironmental

disturbances. It was found that sensory feedback could compensate forboth the bias of the chip and environmental perturbations. FIGS. 6(A)and (B) show restoration to a nominal orbit after perturbation in intactand lesioned cases. In the intact case, a perturbation at cycle ‘2’ leadto outlying trajectories, but the trajectory was quickly restored to thenominal orbit. In the lesioned case, removal of sensory feedback causedthe chip bias to destroy the trajectory of the leg. The gait quicklydeteriorated.

Thus, the present inventors have provided what they believe to be thefirst experimental results of an adaptive VLSI neural chip controlling arobotic leg. Using sensory feedback, the circuit adapted the gait of theleg to compensate both for chip bias and environmental perturbations.This work represents the first experimental results known to the presentinventors of an adaptive VLSI neural chip controlling a robot leg. Theexperimentation set forth herein establishes a successful workinghardware implementation of a CPG-based model according to the invention.The data of FIGS. 4,5 and 6 establish that a VLSI chip according to theinvention having only 4 neurons and occupying less than 0.4 square nmcontrolled a leg running on a treadmill.

The data also reflect success in providing running as a dynamic process,for the under-actuated robotic leg of Example 1. In the resultspresented here, the energy injected into the hip was sufficient toexcite an orbital trajectory of the knee as well. The hip, knee, andfoot sensor orbit appear remarkably stable when the CPG circuit wasstabilized using sensory feedback. The data of FIGS. 4, 5 and 6 reflectthat control of a running leg using a VLSI CPG chip. The data furtherreflect successful application of a neuromorphic approach to build acomplete artificial nervous system to control a robot. The functioningof the robot of Example 1 confirmed the implementation of an adaptiveCPG model in a compact analog VLSI circuit. The experimentation confirmsthat the circuit in use has adaptive properties that allow it to tuneits behavior based on sensory feedback. The adaptive CPG chip accordingto the present invention is thought to be the first functioning adaptiveCPG chip. The results of the experimentation suggest that inexpensive,low power and compact controllers for walking, flying and swimmingmachines and other movement machines may be constructed using thepresent invention.

Also, the experimentation confirms that the invention provides afunctioning chip, based on principles of the locomotor-control circuitsin the nervous system, that mimics many of the features of a biologicalCPG. A circuit according to the invention was shown to control arobotics leg running on a circular treadmill. Furthermore, a circuitaccording to the invention was shown to use sensory feedback tostabilize the rhythmic movements of the leg The experimentation confirmsthat the invention may provide inexpensive circuits that are adaptable,controllable and able to generate complex, coordinated movements. Theexperimentation establishes that the present invention providesself-adaptation in a CPG-based system based on sensory input.

While the invention has been described in terms of its preferredembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims.

1. A central pattern generator-based system for controlling at least onemechanical limb, comprising at least one mechanical limb; and anon-biological central pattern generator that autonomously generates arhythmic pattern of commands for controlling repetitive cyclicalmovement of the at least one mechanical limb wherein commands areadapted as a function of sensory feedback.
 2. (canceled)
 3. The centralpattern generator-based system of claim 1, further including: a systemfor phase adjustment of the central pattern generator based on at leastone sensory trigger in or derived from sensory feedback; and a systemfor controlling firing frequency of motoneurons as a function of thesensory feedback or the sensory trigger.
 4. The central patterngenerator-based system of claim 1, further including at least one memorydevice.
 5. The central pattern generator-based system of claim 4,wherein the memory device controls adaptation of output from the centralpattern generator.
 6. The central pattern generator-based system ofclaim 5, wherein the output includes integrate-and-fire neurons.
 7. Thecentral pattern generator-based system of claim 1, wherein the system isat least one chip.
 8. The central pattern generator-based system ofclaim 7, including at least one chip containing electronic analogues ofbiological neurons, synapses and time-constraints.
 9. The centralpattern generator-based system of claim 7, including at least one chipthat includes dynamic memories and phase modulators.
 10. The centralpattern generator-based system of claim 1, wherein the system is anon-linear oscillator including electronic analogues of biologicalneurons, synapses and time-constraints, dynamic memories and phasemodulators.
 11. The central pattern generator-based system of claim 7,wherein the system includes at least one chip in which components areintegrated with hardwired or programmable circuits.
 12. The centralpattern generator-based system of claim 1, wherein the central patterngenerator is a distributed system of at least two non-linearoscillators.
 13. The central pattern generator-based system of claim 12,wherein the distributed system includes at least one neuron physicallycoupled to a neuron or a sensory input.
 14. The central patterngenerator-based system of claim 12, wherein the distributed systemincludes at least two neurons physically coupled to each other, toanother neuron, or to a sensory input.
 15. The central patterngenerator-based system of claim 14, wherein phasic coupling is in-phase,180 degrees out of phase, or any number of degrees out of phase.
 16. Thecentral pattern generator-based system of claim 14, wherein phasiccoupling is based on rhythmic movement application.
 17. The centralpattern generator-based system of claim 14, including a phase controlcircuit.
 18. The central pattern generator-based system of claim 14,including at least one integrate-and-fire spiking motoneuron driven bythe physically coupled neurons.
 19. The central pattern generator-basedsystem of claim 1, including at least one muscle.
 20. The centralpattern generator-based system of claim 1, wherein the system is arobot.
 21. The central pattern generator-based system of claim 7,wherein the system includes a central pattern generator chip and atleast one biological neuron.
 22. The central pattern generator-basedsystem of claim 21, including multiple chips.
 23. The central patterngenerator-based system of claim 1, including at least one sensor forcollecting sensory feedback.
 24. The central pattern generator system ofclaim 23, including a system for phase adjustment of the central patterngenerator based on at least one sensory trigger in the received sensoryfeedback.
 25. The central pattern generator-based system of claim 1,wherein the sensory feedback is received from the at least onemechanical limb.
 26. The central pattern generator-based system of claim1, wherein the sensory feedback is received from a sensing modality. 27.A central pattern generator-based system for controlling a biologicalsystem for rhythmic movement, comprising an interface with a biologicalsystem that can provide sensory feedback from said biological system;and a non-biological central pattern generator that autonomouslygenerates commands including a rhythmic pattern of commands forcontrolling repetitive cyclical movements of the biological systemwherein said commands and said rhythmic pattern of commands are adaptedas a function of sensory feedback. 28.-29. (canceled)
 30. The centralpattern generator-based system of claim 27, further including at leastone memory device.
 31. The central pattern generator-based system ofclaim 30, wherein the memory device controls adaptation of output fromthe central pattern generator.
 32. The central pattern generator-basedsystem of claim 31, wherein the output includes integrate-and-fireneurons.
 33. The central pattern generator-based system of claim 27,wherein the system is at least one chip.
 34. The central patterngenerator-based system of claim 33, including at least one chipcontaining electronic analogues of biological neurons, synapses andtime-constraints.
 35. The central pattern generator-based system ofclaim 33, including at least one chip that includes dynamic memories andphase modulators.
 36. The central pattern generator-based system ofclaim 27, wherein the system is a non-linear oscillator includingelectronic analogues of biological neurons, synapses andtime-constraints, dynamic memories and phase modulators.
 37. The centralpattern generator-based system of claim 33, wherein the system includesat least one chip in which components are integrated with hardwired orprogrammable circuits.
 38. The central pattern generator-based system ofclaim 27, wherein the central pattern generator is a distributed systemof at least two non-linear oscillators.
 39. The central patterngenerator-based system of claim 38, wherein the distributed systemincludes at least one neuron physically coupled to a neuron or a sensoryinput.
 40. The central pattern generator-based system of claim 38,wherein the distributed system includes at least two neurons physicallycoupled to each other, to another neuron, or to a sensory input.
 41. Thecentral pattern generator-based system of claim 40, wherein phasiccoupling is in-phase, 180 degrees out of phase, or any number of degreesout of phase.
 42. The central pattern generator-based system of claim40, wherein phasic coupling is based on rhythmic movement application.43. The central pattern generator-based system of claim 40, including aphase control circuit.
 44. The central pattern generator-based system ofclaim 40, including at least one integrate-and-fire spiking motoneurondriven by the physically coupled neurons.
 45. The central patterngenerator-based system of claim 27, including at least one muscle. 46.The central pattern generator-based system of claim 33, wherein thesystem includes a central pattern generator chip and at least onebiological neuron.
 47. The central pattern generator-based system ofclaim 46, including multiple chips.
 48. The central patterngenerator-based system of claim 27, including at least one sensor forcollecting sensory feedback.
 49. The central pattern generator system ofclaim 48, including a system for phase adjustment of the central patterngenerator based on at least one sensory trigger in the received sensoryfeedback.
 50. The central pattern generator-based system of claim 27,wherein the sensory feedback is received from the at least onebiological limb.
 51. The central pattern generator-based system of claim27, wherein the sensory feedback is received from a sensing modality.52.-54. (canceled)
 55. A robotics system comprising: (a) a centralpattern generator-based system that mimics a biological central patterngenerator to autonomously generate commands including a rhythmic patternof commands; and (b) at least one sensory device providing signals foradaptation of said commands and said rhythmic pattern of commands. 56.The robotics system of claim 55, wherein the central patterngenerator-based system receives sensory input from the at least onesensory device.
 57. An autonomous movement device for providing rhythmiccontrol, wherein the autonomous movement device comprises: anon-biological central pattern generator that generates a pattern ofrhythmic control commands wherein said control commands are adapted as afunction of sensory feedback.
 58. The autonomous movement device ofclaim 57, including at least one mechanical limb.
 59. The autonomousdevice of claim 58 wherein the limb is a leg, arm, wing or appendage forswimming.
 60. The movement device of claim 58 including at least twolimbs.
 61. The movement device of claim 57, wherein the device is abreathing controller.
 62. The movement device of claim 57, wherein thedevice is a pacemaker.
 63. The movement device of claim 57, wherein thedevice is a running device.
 64. A non-biological central patterngenerator for autonomously producing a rhythmic pattern of outputsignals comprising: a memory device; and a system for manipulatingneural phasic relationships of said pattern of autonomously generatedoutput signals. 65.-67. (canceled)